21 citations · 57 across the 17 of their papers we have counts for
17 papers
Qwen-Image Technical Report
Chenfei Wu, Jiahao Li, Jingren Zhou +36
We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address th…
Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs
Xiao Xu, Libo Qin, Wanxiang Che +1
Two-Tower Vision--Language Models (VLMs) have demonstrated strong performance across various downstream VL tasks. While BridgeTower further enhances performance by building bridges…
Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models
Xiao Xu, Tianhao Niu, Yuxi Xie +3
Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that in…
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
Yunlong Feng, Dechuan Teng, Yang Xu +5
Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable. Previous work has primarily focused on enhancing de…
MCoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought
Qiguang Chen, Libo Qin, Jin Zhang +3
Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Neve…
Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding
Dechuan Teng, Chunlin Lu, Xiao Xu +2
Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Kno…
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
Xiao Xu, Bei Li, Chenfei Wu +6
Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges bet…
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Libo Qin, Qiguang Chen, Xiao Xu +2
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and…
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Wenbo Pan, Qiguang Chen, Xiao Xu +2
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate th…
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Yunlong Feng, Bohan Li, Libo Qin +2
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source dom…
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
Bohan Li, Xiao Xu, Xinghao Wang +6
Existing image augmentation methods consist of two categories: perturbation-based methods and generative methods. Perturbation-based methods apply pre-defined perturbations to augm…
BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning
Xiao Xu, Chenfei Wu, Shachar Rosenman +3
Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-mod…
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Xiao Xu, Libo Qin, Kaiji Chen +3
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its…
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Libo Qin, Tianbao Xie, Shijue Huang +3
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation. However, in contrast to the r…
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Fuxuan Wei, Tianbao Xie +3
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive ap…
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Libo Qin, Xiao Xu, Wanxiang Che +2
Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a cert…
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Xiao Xu, Wanxiang Che +1
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the sing…